






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


_UpperCAmelCase      :		Optional[Any] 					=     logging.get_logger(__name__)
class 			lowerCAmelCase_							(					snake_case_       ):
							UpperCamelCase_		:Tuple		   =     ['input_features', 'is_longer']




							def __init__( self  : int			,				SCREAMING_SNAKE_CASE_  : List[str]=64			,				SCREAMING_SNAKE_CASE_  : Tuple=48_000			,				SCREAMING_SNAKE_CASE_  : Tuple=480			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]=10			,				SCREAMING_SNAKE_CASE_  : List[Any]=1_024			,				SCREAMING_SNAKE_CASE_  : List[str]=0.0			,				SCREAMING_SNAKE_CASE_  : Optional[int]=False			,				SCREAMING_SNAKE_CASE_  : float = 0			,				SCREAMING_SNAKE_CASE_  : float = 14_000			,				SCREAMING_SNAKE_CASE_  : int = None			,				SCREAMING_SNAKE_CASE_  : str = "fusion"			,				SCREAMING_SNAKE_CASE_  : str = "repeatpad"			,				**SCREAMING_SNAKE_CASE_  : int			,				):
											super().__init__(
											    feature_size=lowerCAmelCase__			,				sampling_rate=lowerCAmelCase__			,				padding_value=lowerCAmelCase__			,				return_attention_mask=lowerCAmelCase__			,				**lowerCAmelCase__			,				)
											lowerCAmelCase__         =    top_db
											lowerCAmelCase__         =    truncation
											lowerCAmelCase__         =    padding
											lowerCAmelCase__         =    fft_window_size
											lowerCAmelCase__         =    (fft_window_size >> 1) + 1
											lowerCAmelCase__         =    hop_length
											lowerCAmelCase__         =    max_length_s
											lowerCAmelCase__         =    max_length_s * sampling_rate
											lowerCAmelCase__         =    sampling_rate
											lowerCAmelCase__         =    frequency_min
											lowerCAmelCase__         =    frequency_max
											lowerCAmelCase__         =    mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins			,				num_mel_filters=lowerCAmelCase__			,				min_frequency=lowerCAmelCase__			,				max_frequency=lowerCAmelCase__			,				sampling_rate=lowerCAmelCase__			,				norm=lowerCAmelCase__			,				mel_scale='''htk'''			,				)
											lowerCAmelCase__         =    mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins			,				num_mel_filters=lowerCAmelCase__			,				min_frequency=lowerCAmelCase__			,				max_frequency=lowerCAmelCase__			,				sampling_rate=lowerCAmelCase__			,				norm='''slaney'''			,				mel_scale='''slaney'''			,				)




							def     __snake_case       ( self  : str						):
											lowerCAmelCase__         =    copy.deepcopy(self.__dict__						)
											lowerCAmelCase__         =    self.__class__.__name__
											if "mel_filters" in output:
															del output["mel_filters"]
											if "mel_filters_slaney" in output:
															del output["mel_filters_slaney"]
											return output




							def     __snake_case       ( self  : int			,				SCREAMING_SNAKE_CASE_  : np.array			,				SCREAMING_SNAKE_CASE_  : Optional[np.array] = None						):
											lowerCAmelCase__         =    spectrogram(
											    lowerCAmelCase__			,				window_function(self.fft_window_size			,				'''hann'''						)			,				frame_length=self.fft_window_size			,				hop_length=self.hop_length			,				power=2.0			,				mel_filters=lowerCAmelCase__			,				log_mel='''dB'''			,				)
											return log_mel_spectrogram.T




							def     __snake_case       ( self  : Tuple			,				SCREAMING_SNAKE_CASE_  : List[str]			,				SCREAMING_SNAKE_CASE_  : Optional[int]			,				SCREAMING_SNAKE_CASE_  : int						):
											lowerCAmelCase__         =    np.array_split(list(range(0			,				total_frames - chunk_frames + 1						)						)			,				3						)
											if len(ranges[1]						) == 0:
															# if the audio is too short, we just use the first chunk
															lowerCAmelCase__         =    [0]
											if len(ranges[2]						) == 0:
															# if the audio is too short, we just use the first chunk
															lowerCAmelCase__         =    [0]
											# randomly choose index for each part
											lowerCAmelCase__         =    np.random.choice(ranges[0]						)
											lowerCAmelCase__         =    np.random.choice(ranges[1]						)
											lowerCAmelCase__         =    np.random.choice(ranges[2]						)

											lowerCAmelCase__         =    mel[idx_front : idx_front + chunk_frames, :]
											lowerCAmelCase__         =    mel[idx_middle : idx_middle + chunk_frames, :]
											lowerCAmelCase__         =    mel[idx_back : idx_back + chunk_frames, :]

											lowerCAmelCase__         =    torch.tensor(mel[None, None, :]						)
											lowerCAmelCase__         =    torch.nn.functional.interpolate(
											    lowerCAmelCase__			,				size=[chunk_frames, 64]			,				mode='''bilinear'''			,				align_corners=lowerCAmelCase__						)
											lowerCAmelCase__         =    mel_shrink[0][0].numpy()
											lowerCAmelCase__         =    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]			,				axis=0						)
											return mel_fusion




							def     __snake_case       ( self  : Tuple			,				SCREAMING_SNAKE_CASE_  : np.array			,				SCREAMING_SNAKE_CASE_  : Union[str, Any]			,				SCREAMING_SNAKE_CASE_  : int			,				SCREAMING_SNAKE_CASE_  : Optional[Any]						):
											if waveform.shape[0] > max_length:
															if truncation == "rand_trunc":
																			lowerCAmelCase__         =    True
																			# random crop to max_length (for compatibility) -> this should be handled by self.pad
																			lowerCAmelCase__         =    len(lowerCAmelCase__						) - max_length
																			lowerCAmelCase__         =    np.random.randint(0			,				overflow + 1						)
																			lowerCAmelCase__         =    waveform[idx : idx + max_length]
																			lowerCAmelCase__         =    self._np_extract_fbank_features(lowerCAmelCase__			,				self.mel_filters_slaney						)[None, :]
															elif truncation == "fusion":
																			lowerCAmelCase__         =    self._np_extract_fbank_features(lowerCAmelCase__			,				self.mel_filters						)
																			lowerCAmelCase__         =    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																			lowerCAmelCase__         =    mel.shape[0]
																			if chunk_frames == total_frames:
																							# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																							# In this case, we just use the whole audio.
																							lowerCAmelCase__         =    np.stack([mel, mel, mel, mel]			,				axis=0						)
																							lowerCAmelCase__         =    False
																			else:
																							lowerCAmelCase__         =    self._random_mel_fusion(lowerCAmelCase__			,				lowerCAmelCase__			,				lowerCAmelCase__						)
																							lowerCAmelCase__         =    True
															else:
																			raise NotImplementedError(f'data_truncating {truncation} not implemented'						)

											else:
															lowerCAmelCase__         =    False
															# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
															if waveform.shape[0] < max_length:
																			if padding == "repeat":
																							lowerCAmelCase__         =    int(max_length / len(lowerCAmelCase__						)						)
																							lowerCAmelCase__         =    np.stack(np.tile(lowerCAmelCase__			,				n_repeat + 1						)						)[:max_length]
																			if padding == "repeatpad":
																							lowerCAmelCase__         =    int(max_length / len(lowerCAmelCase__						)						)
																							lowerCAmelCase__         =    np.stack(np.tile(lowerCAmelCase__			,				lowerCAmelCase__						)						)
																			lowerCAmelCase__         =    np.pad(lowerCAmelCase__			,				(0, max_length - waveform.shape[0])			,				mode='''constant'''			,				constant_values=0						)

															if truncation == "fusion":
																			lowerCAmelCase__         =    self._np_extract_fbank_features(lowerCAmelCase__			,				self.mel_filters						)
																			lowerCAmelCase__         =    np.stack([input_mel, input_mel, input_mel, input_mel]			,				axis=0						)
															else:
																			lowerCAmelCase__         =    self._np_extract_fbank_features(lowerCAmelCase__			,				self.mel_filters_slaney						)[None, :]

											return input_mel, longer




							def __call__( self  : int			,				SCREAMING_SNAKE_CASE_  : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,				SCREAMING_SNAKE_CASE_  : str = None			,				SCREAMING_SNAKE_CASE_  : Optional[str] = None			,				SCREAMING_SNAKE_CASE_  : Optional[int] = None			,				SCREAMING_SNAKE_CASE_  : Optional[int] = None			,				SCREAMING_SNAKE_CASE_  : Optional[Union[str, TensorType]] = None			,				**SCREAMING_SNAKE_CASE_  : Any			,				):
											lowerCAmelCase__         =    truncation if truncation is not None else self.truncation
											lowerCAmelCase__         =    padding if padding else self.padding

											if sampling_rate is not None:
															if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    f'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
																			    f' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
																			    f' was sampled with {self.sampling_rate} and not {sampling_rate}.'						)
											else:
															logger.warning(
															    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
															    '''Failing to do so can result in silent errors that might be hard to debug.'''						)

											lowerCAmelCase__         =    isinstance(lowerCAmelCase__			,				np.ndarray						) and len(raw_speech.shape						) > 1
											if is_batched_numpy and len(raw_speech.shape						) > 2:
															raise ValueError(f'Only mono-channel audio is supported for input to {self}'						)
											lowerCAmelCase__         =    is_batched_numpy or (
											    isinstance(lowerCAmelCase__			,				(list, tuple)						) and (isinstance(raw_speech[0]			,				(np.ndarray, tuple, list)						))
											)

											if is_batched:
															lowerCAmelCase__         =    [np.asarray(lowerCAmelCase__			,				dtype=np.floataa						) for speech in raw_speech]
											elif not is_batched and not isinstance(lowerCAmelCase__			,				np.ndarray						):
															lowerCAmelCase__         =    np.asarray(lowerCAmelCase__			,				dtype=np.floataa						)
											elif isinstance(lowerCAmelCase__			,				np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
															lowerCAmelCase__         =    raw_speech.astype(np.floataa						)

											# always return batch
											if not is_batched:
															lowerCAmelCase__         =    [np.asarray(lowerCAmelCase__						)]

											# convert to mel spectrogram, truncate and pad if needed.
											lowerCAmelCase__         =    [
											    self._get_input_mel(lowerCAmelCase__			,				max_length if max_length else self.nb_max_samples			,				lowerCAmelCase__			,				lowerCAmelCase__						)
											    for waveform in raw_speech
											]

											lowerCAmelCase__         =    []
											lowerCAmelCase__         =    []
											for mel, longer in padded_inputs:
															input_mel.append(lowerCAmelCase__						)
															is_longer.append(lowerCAmelCase__						)

											if truncation == "fusion" and sum(lowerCAmelCase__						) == 0:
															# if no audio is longer than 10s, then randomly select one audio to be longer
															lowerCAmelCase__         =    np.random.randint(0			,				len(lowerCAmelCase__						)						)
															lowerCAmelCase__         =    True

											if isinstance(input_mel[0]			,				lowerCAmelCase__						):
															lowerCAmelCase__         =    [np.asarray(lowerCAmelCase__			,				dtype=np.floataa						) for feature in input_mel]

											# is_longer is a list of bool
											lowerCAmelCase__         =    [[longer] for longer in is_longer]

											lowerCAmelCase__         =    {'''input_features''': input_mel, '''is_longer''': is_longer}
											lowerCAmelCase__         =    BatchFeature(lowerCAmelCase__						)

											if return_tensors is not None:
															lowerCAmelCase__         =    input_features.convert_to_tensors(lowerCAmelCase__						)

											return input_features










'''simple docstring'''



import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCAmelCase (    A	:					Optional[Any]       ,		A	:					Optional[int]       ,		A	:					int					):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE       :   str         =      TaConfig.from_json_file(A					)
							print(F"""Building PyTorch model from configuration: {config}"""					)
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      TaForConditionalGeneration(A					)

							# Load weights from tf checkpoint
							load_tf_weights_in_ta(A       ,		A       ,		A					)

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
							model.save_pretrained(A					)


if __name__ == "__main__":
							lowerCAmelCase_  :							Union[str, Any]             =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_  :							Tuple             =    parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




